From Section 4 we have
,
where
is Gaussian with mean
and covariance
.
Therefore
is Gaussian with mean
and covariance
.
To derive the formulas for
and
in this case,
we start by examining the forms of
and
.
Recursively writing
in terms of
,
,
we obtain:
Using Bayes rule,
,
with:
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Note that
can be written
in terms of
for any value of k.
(16) shows this for k=1.
In general:
To evaluate
,
we can use the above form of
in (16) and integrate over
.
Consider two of these integrations, say the integrals over
and
.
By examining the form of this result,
we will be able to produce the general
result. Showing just the terms involving
,
,
with:
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E1 | ![]() |
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Expanding the first term from the exponent in (22),
and dropping constant terms:
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This result shows how the integral over
produces terms
involving
which must be included when performing
the integral over
.
Showing just the
terms involving
for this next integral,
,
with:
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(26) indicates the general form of the equations for
and
.
These equations have
more terms than (23) because in deriving (23)
there were no previous integration results to incorporate.
However, if we define
and
,
then (23) may also be written in the same form as (26).
The preceding derivations used (21) to integrate (16)
over
and
,
and show
the recursive forms of the update equations which appear in (19).
If we start at the ``other end'' of (21), and integrate over
first, then
,
etc., we
obtain similar recursive equations which appear in (17).
Finally, to produce
after integrating over
,
we have
,
with:
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